A mathematical programming approach to optimal Markovian switching of Poisson arrival streams to queueing systems

Motivated by certain situations in manufacturing systems and communication networks, we look into the problem of maximizing the profit in a queueing system with linear reward and cost structure and having a choice of selecting the streams of Poisson arrivals according to an independent Markov chain. We view the system as a MMPP/GI/1 queue and seek to maximize the profits by optimally choosing the stationary probabilities of the modulating Markov chain. We consider two formulations of the optimization problem. The first one (which we call the PUT problem) seeks to maximize the profit per unit time whereas the second one considers the maximization of the profit per accepted customer (the PAC problem). In each of these formulations, we explore three separate problems. In the first one, the constraints come from bounding the utilization of an infinite capacity server; in the second one the constraints arise from bounding the mean queue length of the same queue; and in the third one the finite capacity of the buffer reflect as a set of constraints. In the problems bounding the utilization factor of the queue, the solutions are given by essentially linear programs, while the problems with mean queue length constraints are linear programs if the service is exponentially distributed. The problems modeling the finite capacity queue are non-convex programs for which global maxima can be found. There is a rich relationship between the solutions of the PUT and PAC problems. In particular, the PUT solutions always make the server work at a utilization factor that is no less than that of the PAC solutions.

Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

A fast algorithm for finding frequent episodes in event streams

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence. Recently,we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence, and showed that, such a definition, in addition to yielding computationally efficient algorithms, has some important theoretical properties in connecting frequent episode discovery with HMM learning. This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition. The algorithms presented here are better (by a factor of N, where N denotes the size of episodes being discovered) in terms of both time and space complexities when compared to existing methods for frequent episode discovery. We show through some simulation experiments, that our algorithms are very efficient. The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery.

Lantana camara L. (Verbenaceae) invasion along streams in a heterogeneous landscape

Streams are periodically disturbed due to flooding, act as edges between habitats and also facilitate the dispersal of propagules, thus being potentially more vulnerable to invasions than adjoining regions. We used a landscape-wide transect-based sampling strategy and a mixed effects modelling approach to understand the effects of distance from stream, a rainfall gradient, light availability and fire history on the distribution of the invasive shrub Lantana camara L.(lantana) in the tropical dry forests of Mudumalai in southern India. The area occupied by lantana thickets and lantana stem abundance were both found to be highest closest to streams across this landscape with a rainfall gradient. There was no advantage in terms of increased abundance or area occupied by lantana when it grew closer to streams in drier areas as compared to moister areas. On an average, the area covered by lantana increased with increasing annual rainfall. Areas that experienced greater number of fires during 1989-2010 had lower lantana stem abundance irrespective of distance from streams. In this landscape, total light availability did not affect lantana abundance. Understanding the spatially variable environmental factors in a heterogeneous landscape influencing the distribution of lantana would aid in making informed management decisions at this scale.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Micro-scale anaerobic digestion of point source components of organic fraction of municipal solid waste

The fermentation characteristics of six specific types of the organic fraction of municipal solid waste (OFMSW) were examined, with an emphasis on properties that are needed when designing plug-flow type anaerobic bioreactors. More specifically, the decomposition patterns of a vegetable (cabbage), fruits (banana and citrus peels), fresh leaf litter of bamboo and teak leaves, and paper (newsprint) waste streams as feedstocks were studied. Individual OFMSW components were placed into nylon mesh bags and subjected to various fermentation periods (solids retention time, SRT) within the inlet of a functioning plug-flow biogas fermentor. These were removed at periodic intervals, and their composition was analyzed to monitor decomposition rates and changes in chemical composition. Components like cabbage waste, banana peels, and orange peels fermented rapidly both in a plug-flow biogas reactor (PFBR) as well as under a biological methane potential (BMP) assay, while other OFMSW components (leaf litter from bamboo and teak leaves and newsprint) fermented slowly with poor process stability and moderate biodegradation. For fruit and vegetable wastes (FVW), a rapid and efficient removal of pectins is the main cause of rapid disintegration of these feedstocks, which left behind very little compost forming residues (2–5%). Teak and bamboo leaves and newsprint decomposed only to 25–50% in 30 d. These results confirm the potential for volatile fatty acids accumulation in a PFBR’s inlet and suggest a modification of the inlet zone or operation of a PFBR with the above feedstocks.

A note on mass transfer in turbulent wall jets

THE PROCESS of mass transfer from saturated porous surfaces virtual origin ; exposed to turbulent air streams finds many practical applitransverse coordinate; cations. In many cases, the air stream will be in the form of a height of nozzle above flat plate--radial wall jet; wall jet over the porous surface. The aerodynamics of both plane and radial wall jets have been investigated in detail and a vast amount of literature is available on the subject [l-3].

Theoretical study of a 16-µm CO2 downstream-mixing gasdynamic laser: A two-dimensional approach

A 16-µm CO2-N2 downstream-mixing gasdynamic laser, where a cold CO2 stream is mixed with a vibrationally excited N2 stream at the exit of the nozzle, is studied theoretically. The flow field is analyzed using a two-dimensional, unsteady, laminar and viscous flow model including appropriate finite-rate vibrational kinetic equations. The analysis showed that local small-signal gain up to 21.75 m−1 can be obtained for a N2 reservoir temperature of 2000 K and a velocity ratio of 1:1 between the CO2 and N2 mixing streams. Applied Physics Letters is copyrighted by The American Institute of Physics.

Catalyzed air oxidation for treatment of thiosalt effulents

Continuous slurry reactor runs of two to four weeks duration were carried out for catalyzed air oxidation of thiosalts under a variety of conditions using poly (4-vinylpyridine) - Cu (II) and quaternized poly (4-vinylpyridine) - Cu (II) catalysts. Results obtained indicate that these catalysts have high activity and relatively long-term catalyst stability for thiosalt waste streams of < 1000 ppm thiosalt level. Using 2% (w/w) slurries of the poly (4-vinylpyridine) Cu (II) catalyst, effective oxidation of 700 ppm S2O32− influent to an effluent of < 100 ppm total thio-salts can be carried out continuously for at least one month when operating at 20 to 30°C with solution flow rates of$˜1l/h and aeration of 1300 XXX/h using a two-stage reactor system comprised of 12 l reactors. At higher thiosalt influent levels (i.e. > 1600 ppm) increased reaction temperatures enable depletion to < 100 ppm thiosalt effluent levels for up to one week of continuous operation. The catalysts deactivate much more readily at these higher influent levels as a result of greater copper losses and appreciable adsorption of S2O32− and S4O62−. The behaviour of continuous slurry reactors employed in the experimental studies, by use of batch reaction data for the poly (4-vinylpyridine) Cu (II) catalyzed oxidation of thiosalts, can be modelled successfully. Quaternized poly (4-vinylpyridine) Cu (II) catalyst has good long-term stability and copper losses are very low. The poly (4-vinylpyridine) Cu (II) catalyst, however, is susceptible to appreciable oxidation of the polymer matrix on long-term usage. This oxidation of the polymer matrix results in a substantial loss in the activity of the regenerated catalyst.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Architecture of a polymorphic ASIC for interoperability across multi-mode H.264 decoders

Run-time interoperability between different applications based on H.264/AVC is an emerging need in networked infotainment, where media delivery must match the desired resolution and quality of the end terminals. In this paper, we describe the architecture and design of a polymorphic ASIC to support this. The H.264 decoding flow is partitioned into modules, such that the polymorphic ASIC meets the design goals of low-power, low-area, high flexibility, high throughput and fast interoperability between different profiles and levels of H.264. We demonstrate the idea with a multi-mode decoder that can decode baseline, main and high profile H.264 streams and can interoperate at run.time across these profiles. The decoder is capable of processing frame sizes of up to 1024 times 768 at 30 fps. The design synthesized with UMC 0.13 mum technology, occupies 250 k gates and runs at 100 MHz.

Impact of riparian land use on stream insects of Kudremukh National Park, Karnataka state, India

The impact of riparian land use on the stream insect communities was studied at Kudremukh National Park located within Western Ghats, a tropical biodiversity hotspot in India. The diversity and community composition of stream insects varied across streams with different riparian land use types. The rarefied family and generic richness was highest in streams with natural semi evergreen forests as riparian vegetation. However, when the streams had human habitations and areca nut plantations as riparian land use type, the rarefied richness was higher than that of streams with natural evergreen forests and grasslands. The streams with scrub lands and iron ore mining as the riparian land use had the lowest rarefied richness. Within a landscape, the streams with the natural riparian vegetation had similar community composition. However, streams with natural grasslands as the riparian vegetation, had low diversity and the community composition was similar to those of paddy fields. We discuss how stream insect assemblages differ due to varied riparian land use patterns, reflecting fundamental alterations in the functioning of stream ecosystems. This understanding is vital to conserve, manage and restore tropical riverine ecosystems.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

Two-stream instability in plasma

The problem of two-stream instability in plasma is studied by specifying the importance of initial magnetic field associated with the motion of the charged particles and the boundary effects. In Part I the accurate initial steady state is studied when the streams of electrons and ions move with different uniform speeds in plasmas with plane and cylindrical geometry. In Part II, in order to show the effects of finiteness and inhomogeneity of the system, small transverse plasma oscillations are studied in the case of plane plasmas. The role of plasma-sheath oscillations at the boundaries is found to be very important in driving the instabilities associated with the electromagnetic modes. The numerical estimates of the growth rates of the instability are given for the specific case of the physical data in discharge tubes.

Performance analysis of a slotted-ALOHA protocol on a capture channel with fading

We consider the slotted ALOHA protocol on a channel with a capture effect. There are M streams are stationary ergodic. Under the same rate conditions, for general regenerative arrival streams, we obtain the rates of convergence to stationarity, finiteness of stationary moments and various functional limit theorems. Our arrival streams contain all the traffic models suggested in the recent literature, including the ones which display long range dependence. We also obtain bounds on the stationary moments of waiting times which can be tight under realistic conditions. Finally, we obtain several results on the transient performance of the system, e.g., first time to overflow and the limits of the overflow process. We also extend the above results to the case of a capture channel exhibiting Markov modulated fading. Most of our results and proofs will be shown to hold also for the slotted ALOHA protocol without capture.